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Risk and Safety Management in Mega Construction Projects: Trends, Contracts...
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Risk and Safety Management in Mega Construction Projects: Trends, Contracts and Technologies

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 9943

Special Issue Editors

Prof. Dr. Tingya Hsieh

E-Mail Website
Guest Editor
Graduate Institute of Construction Engineering and Management, National Central University, Taoyuan 32001, Taiwan
Interests: construction economics; construction law; turnkey contracting
Dr. Pin-Chao Liao

E-Mail Website
Guest Editor
Department of Construction Management, School of Civil Engineering, Tsinghua University, Beijing 100084, China
Interests: safety and quality; neuromanagement; construction risks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the development of modern technologies and approaches, risk and safety performance have displayed a slight improvement in the last decade. Studies started to investigate the root cause of such stagnation, particularly pertaining to mega construction projects. 

Construction entities have faced tremendous supply chain pressure under the COVID-19 pandemic. The global energy supply and human resources are insufficient because of the unstable domestic political situation. Internally, the clients’ increased workmanship/quality/schedule requirements posed significant management pressures against the contractors. Undoubtedly, the abovementioned factors impose massive restrictions on risk and safety management. Therefore, a strategic breakthrough is critical for the sustainable development of the construction industry.

To acquire the focus of this research streamline, this topic focuses on the theories and technologies that echo contemporary issues. Contributions to other important engineering-related domains, such as project governance, delivery, and contracting will also be considered. Original research, reviews, policy and practice reviews, book reviews, methods, hypothesis and theories, opinions, perspectives, and conceptual analyses are all welcome. The scope of this Special Issue includes the development of theories or technologies with clear links to the contemporary risk and safety issues in the areas of:

  1. Decision science;
  2. Evaluation science;
  3. Management science;
  4. Big Data;
  5. Machine learning / Smart construction;
  6. Advanced project delivery methods;
  7. Contract and legal Issues.

Prof. Dr. Tingya Hsieh
Dr. Pin-Chao Liao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • construction industry
  • safety
  • contemporary issues
  • theories and technologies

Published Papers (4 papers)

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Research

26 pages, 3146 KiB  
Article
Assessing the Degradation of Safety Management Performance in Large Construction Projects: An Investigation and Decision Model Based on Complex Network Modeling
by Haidong Guo, Xingshan Gao, Qiangqiang Lin and Baosheng Gao
Sustainability 2023, 15(16), 12283; https://doi.org/10.3390/su151612283 - 11 Aug 2023
Viewed by 1029
Abstract
Modern safety control theory suggests that the accumulation of safety management defects at the organizational level can lead to a degradation in the overall safety management performance. This problem is exacerbated by the increasing complexity of safety management in large construction projects. The [...] Read more.
Modern safety control theory suggests that the accumulation of safety management defects at the organizational level can lead to a degradation in the overall safety management performance. This problem is exacerbated by the increasing complexity of safety management in large construction projects. The theoretical frameworks proposed by existing studies can provide generalized guidance for identifying safety management defects, but they are not flexible enough to address the complexity of a safety management system (SMS) in specific large construction projects. This study proposed an investigation and decision model based on a complex network model of SMSs. The main purpose was to accurately assess the degradation of safety management performance through the comprehensive identification of safety management defects for large construction projects. The functional components and their interactions in SMSs were graphically represented in a complex network using the fuzzy DEMATEL technique. Based on this, deep-seated safety management defects were identified by tracing the path of influence between the functional components and their roots. Furthermore, the results of this identification were used to support the assessment of the degradation of the safety performance of the overall SMS. The proposed model was verified with a large-scale wastewater treatment plant construction project in Lanzhou City, China. The degradation of the functional components could be presented in a complex visual network map to facilitate an understanding of the weak points or risk-sensitive areas throughout the SMS. Especially in the case of false safety perceptions, deep-seated safety management defects can be identified in time to prevent a sudden collapse of the SMS through early warnings. In addition, it also facilitates timely short-term improvement strategies and systematic long-term improvement strategies for long-term sustainability and increased resilience. Full article
(This article belongs to the Special Issue Risk and Safety Management in Mega Construction Projects: Trends, Contracts and Technologies)
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21 pages, 4951 KiB  
Article
EEG-Based Performance-Driven Adaptive Automated Hazard Alerting System in Security Surveillance Support
by Xiaoshan Zhou and Pin-Chao Liao
Sustainability 2023, 15(6), 4812; https://doi.org/10.3390/su15064812 - 8 Mar 2023
Cited by 3 | Viewed by 1766
Abstract
Automated vision-based hazard detection algorithms are being rapidly developed to provide hazard alerts for construction workers. However, these alerting systems often apply a fixed low-beta alerting threshold, which can cause excessive false alarms, followed by distractions and human distrust in automation. In this [...] Read more.
Automated vision-based hazard detection algorithms are being rapidly developed to provide hazard alerts for construction workers. However, these alerting systems often apply a fixed low-beta alerting threshold, which can cause excessive false alarms, followed by distractions and human distrust in automation. In this study, we propose a novel adaptive automated hazard alerting system capable of adjusting alert threshold levels based on environmental scenarios and workers’ hazard recognition performance evaluated using a wearable electroencephalogram (EEG) sensor system. We designed a hazard recognition experiment consisting of multiple hazardous scenarios and acquired behavioral data and EEG signals from 76 construction workers. We used the linear ballistic accumulator model to decompose hazard recognition into several psychological subcomponents and compared them among different scenarios. Subsequently, our proposed strategy includes clustering of participants’ hazard recognition performance levels based on latent profile analysis, wavelet transform of EEG signals, transfer learning for signal classification, and continual learning to improve the robustness of the model in different scenarios. The results show that the proposed method represents a feasible worker-centered adaptive hazard alerting approach. The anticipated system can be leveraged in a real-world wearable headset application that aims to promote proactive hazard intervention and facilitate human trust in automated hazard alerting technologies. Full article
(This article belongs to the Special Issue Risk and Safety Management in Mega Construction Projects: Trends, Contracts and Technologies)
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15 pages, 6528 KiB  
Article
Real-Time Personal Protective Equipment Compliance Detection Based on Deep Learning Algorithm
by Jye-Hwang Lo, Lee-Kuo Lin and Chu-Chun Hung
Sustainability 2023, 15(1), 391; https://doi.org/10.3390/su15010391 - 26 Dec 2022
Cited by 10 | Viewed by 4462
Abstract
The construction industry is one of the most dangerous industries in the world due to workers being vulnerable to accidents, injuries and even death. Therefore, how to effectively manage the appropriate usage of personal protective equipment (PPE) is an important research issue. In [...] Read more.
The construction industry is one of the most dangerous industries in the world due to workers being vulnerable to accidents, injuries and even death. Therefore, how to effectively manage the appropriate usage of personal protective equipment (PPE) is an important research issue. In this study, deep learning is applied to the PPE inspection model to verify whether construction workers are equipped in accordance with the regulations, and this is expected to reduce the probability of related occupational disasters caused by the inappropriate use of PPE. The method is based on the YOLOv3, YOLOv4 and YOLOv7 algorithms to detect worker’s helmets and high-visibility vests from images or videos in real time. The model was trained on a new PPE dataset collected and organized by this study; the dataset contains 11,000 images and 88,725 labels. According to the test results, can achieve a 97% mean average precision (mAP) and 25 frames per second (FPS). The research result shows that the detection and counting data in this method have performed well and can be applied to the real-time PPE detection of workers at the construction job site. Full article
(This article belongs to the Special Issue Risk and Safety Management in Mega Construction Projects: Trends, Contracts and Technologies)
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20 pages, 3100 KiB  
Article
Indoor Safety Monitoring for Falls or Restricted Areas Using Wi-Fi Channel State Information and Deep Learning Methods in Mega Building Construction Projects
by Chih-Hsiung Chang, Mei-Ling Chuang, Jia-Cheng Tan, Chuen-Chyi Hsieh and Chien-Cheng Chou
Sustainability 2022, 14(22), 15034; https://doi.org/10.3390/su142215034 - 14 Nov 2022
Cited by 2 | Viewed by 1617
Abstract
With the trend of sustainable development growing worldwide, both the numbers of new mega building construction projects and renovations to existing high-rise buildings are increasing. At such construction sites, most construction workers can be described as performing various activities in indoor spaces. The [...] Read more.
With the trend of sustainable development growing worldwide, both the numbers of new mega building construction projects and renovations to existing high-rise buildings are increasing. At such construction sites, most construction workers can be described as performing various activities in indoor spaces. The literature shows that the indoor safety protection measures in such construction sites are often imperfect, resulting in an endless stream of incidents such as falls. Thus, this research aims at developing a flexible indoor safety warning system, based on Wi-Fi-generated channel state information (CSI), for monitoring the construction workers approaching restricted areas or floor openings. In the proposed approach, construction workers do not have to carry any sensors, and each indoor space only needs to have the specified Wi-Fi devices installed. Since deep learning methods are employed to analyze the CSI data collected, the total deployment time, including setting up the Wi-Fi devices and performing data collection and training work, has been measured. Efficiency and effectiveness of the developed system, along with further developments, have been evaluated and discussed by 12 construction safety experts. It is expected that the proposed approach can be enhanced to accommodate other types of safety hazards and be implemented in all mega building construction projects so that the construction workers can have safer working environments. Full article
(This article belongs to the Special Issue Risk and Safety Management in Mega Construction Projects: Trends, Contracts and Technologies)
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